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The rapid decline in the newspaper industry has had its strongest impact on local and regional newspapers. This has meant that the populations of entire regions are finding it hard to access news relevant to where they live. This has not removed their desire for local news, simply the principal platform on which it used to be delivered. This platform has now become the Internet, with local news  being provided by a multitude of different sources.

Although online services currently exist which attempt to collect this news and  organise it by location, they fail to deliver this information categorised by topic. While the process for consuming news has changed, there still remains a strong preference to see articles organised into the various sections that would be found in a printed newspaper.

This thesis explores the creation of a fully automated online local newspaper generator which tackles the issue raised above. This involved the research and implementation of techniques to collect and extract articles from online news sources, as well as to automatically classify them by both topic and location. 

A neighbouring location detection method was also implemented to automatically search for news about the surrounding area, if an insufficient amount of information was acquired to produce a complete newspaper from the desired location alone. The emphasis of this thesis has been on ensuring that all methods remain generic, allowing the product to be scaled and expanded. 

The output of this project is a prototype which, if developed into a commercial product, would provide a service currently unavailable anywhere else. The techniques employed also have the potential to produce commercial software with widespread applications for extraction and classification of text in a cost-effective manner. 

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\subsection*{Main Contributions and Achievements}

\begin{itemize}
	\item Designed and implemented a novel approach to online article content extraction with a success rate comparable or superior to all currently published methods.
	\item Implemented a topic classification method based on Support Vector Machines to automatically organise articles into the various sections of a newspaper.
	\item Implemented a novel approach to location classification, also based on Support Vector Machines, using training data rather than pre-defined geographical taxonomies.
	\item Developed a location ranking algorithm to order articles according to their relevance to the desired location.
	\item Designed and implemented a quick and efficient method of detecting the nearest neighbours to a particular location using GPS co-ordinates.
	\item Ensured that all methods remained generic, allowing the product to be to be scaled to cover any topic or geographical area.

\end{itemize}


